As resource-intensive and latency-sensitive applications continue to expand, the integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) has emerged as a viable solution, offering flexible, on-demand services for mobile users (MUs) without reliance on terrestrial infrastructure. The adoption of non-orthogonal multiple access (NOMA) further reduces latency by allowing MUs to offload tasks simultaneously over a single subchannel. However, many existing offloading methods do not explicitly incorporate a priority-based task scheduling mechanism and instead optimize task execution based on system constraints such as latency or energy consumption. To bridge this gap, we propose a QoS-oriented task offloading scheme that systematically optimizes task scheduling. We formulate an average system utility maximization problem that jointly optimizes UAVs’ 3D trajectories, MU association, task offloading ratios, and resource allocation. The optimization problem is inherently complex due to its non-convex nature and multiple constraints. To address this, we first employ Lagrange duality to decouple constraints, reducing computational complexity. Subsequently, we propose a novel improved soft actor-critic (ISAC) algorithm, which incorporates a perturbation term into the loss function to guide the training process away from local minima and toward globally optimal solutions. Through extensive simulation, we demonstrate that the ISAC algorithm guarantees convergence and significantly outperforms benchmark methods on offloading transmission rates, task completion rates, and overall system utility.

To solve the problem of the direction of arrival (DOA) maneuvering in acoustic vector sensor (AVS) array, we propose an interactive multi-model Multi-target Multi-Bernoulli (IMM-MeMBer) two-dimensional (2-D) DOA tracking algorithm. The idea of the IMM algorithm is employed to interactively estimate the predicted sampled particles at the expense of calculating the likelihood function of the predicted particles by pseudo-spectrum of multi-signal classification (MUSIC). The proposed method fully considers various possibilities of target motion, which makes the target state estimation more effective. Moreover, the de-noising and exponential weighting of MUSIC pseudo-spectrum improve the likelihood function of particles, thus enhancing the weight of particles in high likelihood region and making these particles more effective in the subsequent resampling process. Simulation results show that the IMM-MeMBer algorithm has better performance than projection approximation subspace tracking of deflation MUSIC (PASTD-MUSIC), MeMBer, particle filter (PF), and random finite set PF (RFS-PF) in the DOA tracking of maneuvering sources.

For the target tracking problem where the number of targets fluctuates with time and the measurement is a point measurement, the random finite set (RFS) class filtering is an available solution. However, in direction of arrival (DOA) tracking, the array observation is a super-positional value, and the tracking performance can be severely impaired if the RFS-based filter approach is applied. As a result, a novel measurement association mapping (NMAP) approach has been presented to cope with the mapping problem between the array observations and sources. Nevertheless, the tracking performance is poor when the number of particles is small. In this paper, a modified delta-Generalized Labeled Multi-Bernoulli (δ-GLMB) DOA tracking particle filter is proposed in combination with the NMAP strategy, which can achieve the same tracking performance with a smaller number of particles by modifying the particles in the δ-GLMB prediction step. Furthermore, the approach is extended to a coprime array and can achieve better DOA tracking performance than a uniform linear array. Simulation experiments validate the effectiveness of the proposed algorithm.

The coprime array has become an essential part of array signal processing, which can obtain a much higher degrees of freedom (DOFs) and larger aperture than that of the conventional approaches. However, this technique is applied in the ideal condition with white Gaussian noise. In this paper, we propose to extend the coprime array to the practical communication scenario by taking impulsive noise and non-circular signals into account. An augmented phased fractional low-order moment (A-PFLOM) is proposed to suppress impulsive noise, and then reduced-dimension MUSIC (RD-MUSIC) subspace techniques are applied for direction of arrival (DOA) information of non-circular sources. Compared to the existing methods, the proposed method has better estimation performance in a wide range of impulsive noise environments.

Direction of arrival (DOA) estimation with coprime array is an important topic in array signal processing. In this letter, the coprime array technique is extended to mixed noise (white Gaussian noise and impulsive noise). Since the second order covariance matrix does not exist in presence of mixed noise, the phased fractional low-order moment (PFLOM) is used to replace the data covariance matrix of the received signals from coprime array, and then a DOA estimation approach based on toeplitz method with coprime array is applied. The feasibility of PFLOM with mixed noise scenario of coprime array is theoretically derived, and the simulation results show that the proposed method has better estimation performance than that of the existing methods, especially in the low generalized signal-to-noise ratio (GSNR) environment.

Direction of arrival (DOA) estimation with co-prime array is a hot issue in array signal processing. By using co-prime array structure, the degrees of freedom has been greatly improved, which can estimate much more sources than that of the conventional array structure. In the scenario of impulsive noise, the high-order (2-order or 4-order) moments of received signal do not exist, fractional low-order moments (FLOM) can be applied. In this paper, the concept of the co-prime array is extended to be applied on FLOM of received signal in presence of impulsive noise. And then MUSIC algorithm is used for DOA estimation. The proposed method is tested on the numerical data. The simulation results prove the effectiveness of the proposed method in impulsive noise scheme.